| id |
acadia16_116 |
| authors |
Davis, Daniel |
| year |
2016 |
| title |
Evaluating Buildings with Computation and Machine Learning |
| doi |
https://doi.org/10.52842/conf.acadia.2016.116
|
| source |
ACADIA // 2016: POSTHUMAN FRONTIERS: Data, Designers, and Cognitive Machines [Proceedings of the 36th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-77095-5] Ann Arbor 27-29 October, 2016, pp. 116-123 |
| summary |
Although computers have significantly impacted the way we design buildings, they have yet to meaningfully impact the way we evaluate buildings. In this paper we detail two case studies where computation and machine learning were used to analyze data produced by building inhabitants. We find that a building’s ‘data exhaust’ provides a rich source of information for longitudinally analyzing people’s architectural preferences. We argue that computation-driven evaluation could supplement traditional post occupancy evaluations. |
| keywords |
spatial analytics, machine learning, post occupancy evaluation |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (610,809 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:55 |
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